Synthetic Sub-datasets Creation
There are lots of typical irregular shape instance segmentation scenes. Consequently, it is impractical to collect a natural dataset for each typical scene. Since it is traditional to study computer vision problems using synthetic data [1]}, [2]}, [3]}, we synthesize five sub-datasets of iShape which include iShape-Branch, iShape-Fence, iShape-Log, iShape-Hanger, and iShape-Wire, by using CG software Blender. Besides, our dataset focuses on the evaluation of instance segmentation methods on irregularly shaped objects, for which the synthetic images can serve well. In particular, we build corresponding 3D models and place them appropriately in Blender with optional random background and lighting environment, optional physic engine, and random camera position. The creation configs of synthesis sub-datasets are listed below. After setting up the scene, we use a ray tracing render engine to render the RGB image. Besides, We build and open source a blender module, bpycv [4]}, to generate instance annotation. We generate 2500 images for each sub-dataset, 2000 for training, 500 for testing.
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